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dc.contributor.authorFrank, Eibe
dc.contributor.authorKibriya, Ashraf Masood
dc.coverage.spatialConference held at Warsaw, Polanden_NZ
dc.identifier.citationKibriya, A. M. & Frank, E. (2007). An Empirical Comparison of Exact Nearest Neighbour Algorithms. In J.N. Kok et al. (Eds.), Proceedings of 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007 (pp. 140-151). Berlin: Springer.en
dc.description.abstractBagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine learning practitioners. Commonly applied in conjunction with decision tree learners to build an ensemble of decision trees, it often leads to reduced errors in the predictions when compared to using a single tree. A single tree is built from a training set of size N. Bagging is based on the idea that, ideally, we would like to eliminate the variance due to a particular training set by combining trees built from all training sets of size N. However, in practice, only one training set is available, and bagging simulates this platonic method by sampling with replacement from the original training data to form new training sets. In this paper we pursue the idea of sampling from a kernel density estimator of the underlying distribution to form new training sets, in addition to sampling from the data itself. This can be viewed as “smearing out” the resampled training data to generate new datasets, and the amount of “smear” is controlled by a parameter. We show that the resulting method, called “input smearing”, can lead to improved results when compared to bagging. We present results for both classification and regression problems.en
dc.sourcePKDD 2007en_NZ
dc.subjectcomputer scienceen
dc.subjectnearest neighbour searchen
dc.subjectMachine learning
dc.titleAn Empirical Comparison of Exact Nearest Neighbour Algorithmsen
dc.typeConference Contributionen
dc.relation.isPartOfProc 11th European Conference on Principles and Practice of Knowledge Discovery in Databasesen_NZ
pubs.volumeLNCS 4702en_NZ

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